CRITERIA FOR ASYMMETRIC PRICE TRANSMISSION MODEL SELECTION BASED ON KULLBACK’S SYMMETRIC DIVERGENCE

Résumé

In econometric modelling of asymmetric price transmission, selection of an optimal model among a collection of candidate models is a critical issue. In order to address the problem, criteria that targets Kullback's Direct Divergence has been extensively used in asymmetric price transmission model selection. An alternative criteria recently introduced by Cavanaugh that targets Kullback's Symmetric Divergence (KIC and KICc) remains unexplored in asymmetric price transmission model selection. In this paper, a Monte Carlo study is conducted to evaluate the relative performance of the recently developed selection criteria based on Kullback's Symmetric Divergence (KIC and KICc) against commonly used alternatives based on Kullback's Direct Divergence (AIC and AICc) in terms of their ability to recover the true asymmetric data generating process. Monte Carlo simulation results indicate that the performance of the model selection methods is influenced by the sample size, the level of asymmetry and the amount of noise in the model used in the application. KICc is comparable to KIC and both outperform AIC and AICc in both small and large samples. At lower noise levels, KICc is comparable to KIC and both outperform AIC and AICc. As difference in asymmetric adjustment parameters or speeds increases KICc is comparable to KIC and both outperform AIC and AICc. These results suggest that criteria based on Kullback's Symmetric Divergence (KICc and KIC), is a very reliable and useful criterion in asymmetric price transmission model selection.

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